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Postpartum depression risk prediction using explainable machine learning algorithms.

Xudong Huang¹, Lifeng Zhang², Chenyang Zhang¹

¹Department of Science and Education, Shenyang Maternity and Child Health Hospital, Shenyang, China.

Frontiers in Medicine

|August 25, 2025

Summary

This summary is machine-generated.

This study developed an explainable machine learning model to predict postpartum depression (PPD) risk. Key factors identified can help healthcare providers identify at-risk mothers for early intervention.

Keywords:

influencing factors machine learning maternal health postpartum depression predictive model

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Using Chronic Social Stress to Model Postpartum Depression in Lactating Rodents

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Published on: June 10, 2013

Area of Science:

Reproductive Medicine
Psychiatry
Machine Learning

Background:

Postpartum depression (PPD) is a significant mental health issue affecting mothers and infants.
Early identification and intervention are crucial for managing PPD.

Purpose of the Study:

To develop an explainable machine learning model for predicting PPD risk.
To identify key predictive factors for PPD.

Main Methods:

Retrospective analysis of 1,065 women's postpartum data.
Feature selection using LASSO regression and Boruta algorithm.
XGBoost model development and evaluation using AUC, accuracy, precision, and specificity.
SHAP for model interpretability.

Main Results:

An 11-variable XGBoost model demonstrated excellent predictive performance (AUC 0.955, accuracy 0.95).
Identified key predictors: weight gain, mother-in-law relationship, sleep quality, marital status, planned pregnancy, fetal sex preference, pregnancy anxiety, pelvic-floor endurance, cervix status, prenatal education, and postpartum care satisfaction.
SHAP analysis provided insights into individual predictions.

Conclusions:

The XGBoost model effectively predicts PPD risk, aiding clinical decision-making.
Explainable AI (SHAP) enhances understanding of PPD causes and prevention strategies.
Improved identification of high-risk individuals can lead to better patient outcomes.